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Toward Critical SpatialThinking in the Social Sciences and Humanities

Michael F. Goodchild and Donald G. Janelle

Department of Geography and the Center for Spatial Studies

University of California, Santa Barbara

Abstract

The integration of geographically referenced information into the conceptual frameworks and

applied uses of the social sciences and humanities has been an ongoing process over the past few

centuries. It has gained momentum in recent decades with advances in technologies for

computation and visualization and with the arrival of new data sources. This article begins with

an overview of this transition, and argues that the spatial integration of information resources and

the cross-disciplinary sharing of analysis and representation methodologies are important forces

for the integration of scientific and artistic expression, and that they draw on core concepts in

spatial (and spatio-temporal) thinking. We do not suggest that this is akin to prior concepts of

unified knowledge systems, but we do maintain that the boundaries to knowledge transfer are

disintegrating and that our abilities in problem solving for purposes of artistic expression and

scientific development are enhanced through spatial perspectives. Moreover, approaches to

education at all levels must recognize the need to impart proficiency in the critical and efficient

application of these fundamental spatial concepts, if students and researchers are to make use of

expanding access to a broadening range of spatialized information and data processing

technologies.

Keywords: spatial concepts, spatial integration, spatial thinking, spatio-temporal knowledge

systems

Corresponding author:

Donald G. Janelle

Department of Geography / Center for Spatial Studies

University of California, Santa Barbara

Santa Barbara, CA 93106-4060, U.S.A.

email: janelle@geog.ucsb.edu

phone: 1 805-893-5267

fax: 1 805-893-3146

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Toward Critical SpatialThinking in the Social Sciences and Humanities

Michael F. Goodchild and Donald G. Janelle

1. Introduction

Why is it that spatial intelligence has not received the same level of interest in education as

reading, written communication, and computational reasoning? After all, society has always

understood that location and geographical patterns of resource distributions and markets can

influence strategic planning in commerce and politics. At a more mundane level, being able to

navigate from one place to another is recognized as critical both to the daily survival of

individuals at local levels and to the geopolitical fortunes of nations and empires. Perhaps these

abilities are instinctive, or acquired at such an early age that they require no attention from our

educational system. Or, are society and its approach to education failing to nurture a fundamental

element of human intelligence?

What we now take for granted as the known distribution of land and water on the Earth's

surface emerged slowly over centuries as the result of huge investments in navies and

expeditions, development of new tools (spatial technologies), compilations of observations, and

working from the known through extrapolation and interpolation to approximations of expected

distributions. This process has demonstrated a high level of commitment to the development and

application of spatial reasoning. But beyond this level of global abstraction lie such topics such

as the hidden dimensions of human settlement patterns, the correlations that might exist between

physical environmental factors and human health, or the prediction of human migratory flows

over time. In these settings, the applications of spatial reasoning take a different turn, combining,

for example, the need for a census or other detailed geo-referenced assemblies of information

about a vast array of phenomena with the recognition that geographical maps and spatial

statistics can become descriptive as well as analytic tools, and that they lie at the heart both of

scientific investigation and discovery and of creative achievements in the arts and humanities.

Profound accomplishments have emerged through spatial thinking about a large number

of applications over the past few centuries. Yet, the systematic development of computational

tools for handling spatial data began only in the 1960s, and today GIS (geographic information

systems) and software for image processing, pattern recognition, and scientific visualization are

in widespread use throughout many disciplines. Functions for the manipulation, analysis, and

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modeling of spatial data are now available in standard statistical and mathematical packages. The

introduction of the Web in the early 1990s helped to make digital images readily sharable, and

pictures of the brain, of Earth from space, and of space from Earth are now important tools in the

neuro-, Earth, and astronomical sciences respectively. GPS and GIS have become ubiquitous

tools in many career fields and in everyday life. Previously, the complexity of formal GIS made

it difficult to use before the high-school level, but now students at the elementary level can

access some of its functions through such services as Google Earth.

2. Spatial turns in the sciences and social sciences

Space appears to have found new theoretical significance in many disciplines in recent years. For

example, in ecology, Tilman and Kareiva (1997) observe that, “although the world is

unavoidably spatial, and each organism is a discrete entity that exists and interacts only with its

immediate neighborhood, these realities have long been ignored by most ecologists because they

greatly complicate field research and modeling”. Dealing with such complications has been the

subject over the past two decades of research efforts in several disciplines, including statistics,

computer science, and geography. Reflecting the fundamental nature of such issues to computer

science, the Association for Computing Machinery approved a new Special Interest Group

(SIGSPATIAL) in 2009 on “issues related to the acquisition, management, and processing of

spatially-related information.”

The flow of information from a host of sensors has grown exponentially in recent years to

the point where the lead editorial in Nature (Anonymous 2008) urged that all observations in the

environmental sciences be georeferenced. The easy availability of GPS enables spatial analysis

and modeling, and addresses the issues of importance to the sciences and humanities that are

posed by the vast legacy of museum and herbarium artifacts for which the location of collection

is poorly recorded (Liu et al., in press). The ability to reason about, and to draw inferences from

spatial pattern has been critical in numerous breakthroughs in epidemiology, starting perhaps

with Snow’s mid-nineteenth century work on cholera (Johnson 2006). Scholten, van de Velde,

and van Manen (2009) provide a recent overview of how location has achieved central

significance in science owing to developments and applications of geospatial technologies.

Coupled with the development of new exploratory tools for mapping and analysis, the

momentum of applications in the social sciences has been especially evident, documented as a

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spatial turn in recent compilations of research by Anselin, Florax, and Rey (2004) on

applications of spatial econometrics; and by Goodchild and Janelle (2004), Scholten, van de

Velde, and van Manen (2009), and Nyerges, Couclelis, and McMaster (in press) on broad-based

applications of spatial methodologies in a range of social sciences. Paul Krugman’s 2008 Nobel

Prize in Economics, for example, was based in part on his reintroduction of the importance of

location in understanding economic activity (Krugman, 1991), attesting to the significant

potential that spatial understanding adds to traditional approaches to the sciences and social

sciences. Contemporary advances in the uses of spatial reasoning in demography and sociology

are profiled by Castro (2007); Voss, White, and Hammer (2006); and Voss (2007). Cromley and

McLafferty (2002) explore applications of GIS in public health research; and the journal

Political Analysis released a special issue on spatial methods in political science (Volume 10 No.

3, 2002).

3. Spatially thinking in the humanities

In the humanities, the Electronic Cultural Atlas Initiative (http://www.ecai.org) has for the past

decade been a significant agent of dissemination of spatial thinking, bringing together scholars

from such disciplines as archaeology, anthropology, history, and religious studies, and from

library and museum archives, to map human cultural heritage and to document the role of place

in society. Another equally important demonstration in the humanities of the value of spatial

perspective is the Spatial History Project at Stanford University (see

http://www.stanford.edu/group/spatialhistory). It brings together scholars at the intersection of

geography and history who use GIS in their research, but also focuses on the harvesting of large

datasets of maps, images, and texts, and their integration to create dynamic, digital visualizations

of change over space and time. The Social Science History Association (http://www.ssha.org)

also regularly features sessions on spatial perspectives in its conferences.

Highlighting the enhanced recent attention to spatial dimensions of scholarship in the

humanities, two important conferences took place in 2009—the Spatial Technologies and

Humanities Conference, sponsored and hosted by the Scholarly Communication Institute (SCI)

at the University of Virginia in Charlottesville, and the GIS in the Humanities and Social

Sciences International Conference 2009 (GISHSS), hosted by the Research Center for Humanities

and Social Sciences of Academia Sinica, in Taipei, with support from Queen's University Belfast,

and Indiana University-Purdue University at Indianapolis.

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The SCI conference resulted in an online report (Rumsey 2009) that clearly articulates on

potential applications of geospatial and mapping technologies, concept mapping, and library

technologies to create virtual worlds for scholarly communication in the arts and humanities. The

conference brought together scholars and academic leaders from different disciplines, academic

libraries, higher education, and information technologies to explore the full life-cycle of

scholarly communication, from research and discovery to analysis, presentation, dissemination,

and persistent access.

The SCI report addresses the importance of a space-time perspective and argumentation

in the interpretations of spatial data, acknowledging that interest in digital forms of space-time

patterns has grown as the accessibility of location-aware devices and services has proliferated.

Mapping services on the Web, especially Google Earth, have become popular gateways for

visualizations of information. Nonetheless, as noted by Jessop (2007), although information

about place and location is an essential part of research in the humanities, sophisticated analytical

programs such as GIS remain slow to accommode the specific concerns of the humanities, a

theme developed more explicitly with respect to history by Boonstra (2009). In general, the " . . .

humanities disciplines most influenced by the linguistic and visual turns in scholarship over the

past few decades have not given priority to critical spatial reasoning" (Rumsey, 2009, p. 4),

possibly reflecting the importance attached to both the real and the imaginary in human culture,

to concern for the qualitative attributes of place, and to representations of nonwestern

perspectives on space and time. Interestingly, geographers at the SCI conference noted a

convergence of interest with the humanities, both in examining how subjective and qualitative

elements of spatial patterns are represented within current GIS applications, and in recognizing

the need for ways to reflect uncertainty and ambiguity.

The GISHSS conference was similarly focused on the integration of cultural and social

nuance in the humanities and social sciences with GIS and other spatial analytic frameworks.

Harris (2009) proposed a general conceptualization of the role of GIS within spatial humanities,

while Gregory (2009) described how both quantitative and qualitative approaches might be

integrated within GIS for interpreting literature and news reports from prior eras. Ayers (2009)

illustrated the potential of dynamic mapping for interpreting historical changes, and

Fotheringham (2009) demonstrated how advanced spatial-analytic methods contribute to the

understanding of population dynamics. Janelle (2009) explored trends and ambiguities in the

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space-time documentation of human behavior and social organization. The importance of spatial

thinking in the social sciences was addressed by Phoenix (2009), while Goodchild (2009) made

the case that critical spatial thinking should be a central theme in education for a world where

information is increasingly seen through geographical filters, is broadly accessibly to the general

population, and is both generated and disseminated voluntarily through digital media.

4. Spatially integrative knowledge systems

Although disciplines have demonstrated and continue to play a critical role in the advancement

of scientific and humanistic understanding, the SCI and GISHSS conferences may represent the

seeds of a fundamental shift from disciplinary to integrative knowledge systems. Examples of

integrative knowledge systems arise from the application of concepts of space and place, and

space and time, having near universal relevance to scholarship in diverse knowledge domains,

with the focus here on their meaning in the humanities and social sciences.

4.1 Integration through concepts of space and place

Throughout our discussion we have seen instances of how space and place are important

elements in social science and humanities interpretations of human well-being and changing

environments. But a recently announced White House place-based initiative (Orszag et al., 2009)

upres attention to the importance of place understanding in formulating sound policy

development and plan implementation. The initiative advocates place-based policies to leverage

". . . investments by focusing resources in targeted places and drawing on the compounding

effect of well-coordinated action . . ." to influence the development of rural and metropolitan

areas and their " . . . function as places to live, work, operate a business, preserve heritage, and

more."

Cummins et al. (2007) provide a case for place-based policy formulation in health

research and health-policy interventions. Arguing that conventional approaches underestimate

the contribution of place to disease risk, they call for relational views to help identify reciprocal

relationships between people and place. Matthews (2008) reinforces this view, documenting how

neighborhood context is an important conditioner of human well-being. Indeed, place has

emerged as an important contextual framework for considering a number of critical societal

issues, noted for example by Janelle and Hodge (2000) in Information, Place, and Cyberspace:

Issues in Accessibility or in the recent Place, Health, and Equity Conference hosted by the

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University of Washington

(http://courses.washington.edu/phequity/Equity_annoucement_combo.pdf ). Whereas the former

drew attention to fundamental linkages between virtual and geographical processes, the latter

was grounded in ". . . place as a social context that is deeply connected to larger patterns of

social advantage and disadvantage [and that] calls for multifaceted conceptions of place as well

as methods that can flexibly encompass geographic location, material form, the meaning-making

of diverse groups, and the dynamics of rapidly changing rural and urban environments."

4.2 Representation and search

There are, of course, other aspects of space and place that link to the research practices of

scholars in the social sciences and humanities. For example, owing to the multidimensional

nature of spatial data, there are technical issues regarding search for digital place-based

information. The Alexandria Digital Library (ADL), developed at the University of California

Santa Barbara (UCSB), was one of the first remotely accessible libraries to support indexing and

search across massive repositories of spatial data. The ADL contributed to the development of

indexing structures for large-scale retrieval and to the development of privacy-secure methods

for querying public spatial data. Further advances in the science of search will entail resolving

problems in managing large volumes of data, tracking data provenance (a key issue when spatial

data are shared and processed across scientific communities), understanding the semantics of

spatial data, and designing methods for achieving interoperability across diverse information

communities (Goodchild et al., 1999).

4.3 Volunteered geographic information

From the perspective of the humanities and social sciences, the Web itself is seen as searchable

repository of potential data sources, some of a traditional nature (e.g., images, works of art,

literature, speeches, maps, census data, or newspapers), and others of a more informal nature,

such as the user-generated content found on social network sites. Today there are opportunities

for anyone to contribute resources (pictures, blogs, etc.) that are automatically geo-referenced by

latitude and longitude and available to be retrieved by others. Goodchild (2007) has termed the

result volunteered geographic information (VGI). Current research on VGI is featured in a

special GeoJournal issue (Elwood, 2008), which explored questions surrounding the uses of such

repositories as keys to social process, environmental understanding, and place-based knowledge.

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This followed a late 2007 research workshop that brought together researchers from industry,

government, and academia to grapple with the complexities of acquisition, validation,

distribution, display, and analysis of VGI data sources, calling attention to both the opportunities

and the challenges that confront scholars in their use (http://ncgia.ucsb.edu/projects/vgi/).

4.4 Spatialization and visualization

Spatialization refers to the construction of abstract spaces of knowledge that can aid in

visualization, pattern detection, and the accumulation of scientific insight (Skupin and Fabrikant,

2003). Thus, things that are not explicitly spatial (e.g., social and kinship networks) may be

rendered graphically for spatial visualization. At the 2009 SCI conference, participants

recognized that ". . . there is an unexplored universe of spatial information implicit in existing

sources, both digital and analog. When 'liberated' from a static analog medium and made legible

to geospatial technologies, a whole new reservoir of information will be available to nourish new

fields of inquiry" (p. 3). For example, historians and literary scholars might explore the

locational and spatial information embedded in nineteenth century novels, railroad timetables,

sound media, or old maps. Another important aspect of this is the issue of respatialization,

defined as the transformation of spatially referenced data from their original geographic

representation to an alternative geographic framework (Goodchild, Anselin, and Deichmann,

1993). For example, data gathered for politically defined units such as counties, states or

provinces, or nations are typically based on a particular spatial representation of the political

units. This representation is often not suitable for simple integration with data collected using

different underlying geographies (e.g., administratively defined regions, watersheds, swaths, or

pixels). Political and administrative representations may also change over time as boundaries

change, units split or merge, or data-gathering organizations change their techniques. If not

appropriately taken into account, such changes can seriously affect the continuity and quality of

time-series data. Because respatialization requires a spatial model, it is an instance of model-

based integration to distinguish it from more general semantic integration based on an ontology.

Respatialization is yet another dimension to the representation of space and place that requires

research and algorithm development to define, for example, relationships between placenames

and coordinates, or to address the shifting reporting zones of censuses. In the case of CIESIN's

Gridded Population of the World (GPW; Tobler et al., 1997), respatialization transforms data

collected for national and subnational administrative units into population totals and densities on

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a grid of spherical quadrilaterals, essentially a set of pixels defined by lines of latitude and

longitude (Tobler et al., 1997), allowing researchers to integrate GPW with other gridded

datasets (e.g., remote sensing data), to reaggregate GPW to alternative spatial units (e.g.,

watersheds, biomes, or metropolitan regions), and to weight other variables by population

characteristics.

4.5 Integration through concepts of space and time

While the term spatial tends to dominate in the literature, the processes that modify systems are

dynamic, and should more correctly be described as spatio-temporal. Moreover, the issues raised

by data embedded in space are similar to those encountered in data embedded in time. In this

context, spatial is used as an umbrella term to include spatio-temporal, as well as geospatial and

geographic when the relevant space is the surface and near-surface of the Earth.

In recent years GPS, video, and other technologies have created a potential wealth of

information about the spatial dynamics of movement by individuals, animals, vehicles, and other

objects through various spaces. Developing theory and associated analytic techniques has proven

more problematic, however, as it has for spatial data more generally. The problems of extracting

useful information from networks of video cameras in human-built environments need to be

resolved before we can understand how buildings and other structures constrain and channel

human spatial behavior or before we can build models to predict the behavior of crowds and to

improve urban designs.

Other aspects of spatial dynamics call for integrative space-time perspectives (Hornsby

and Yuan, 2008; Lin and Batty, 2009), including the diffusion of ideas and innovations, and the

geographical spread of peoples and cultures. In the humanities, the focus turns to relativistic as

opposed to absolute spaces, and to the representation of spaces as they might have been in

historical times or under the influence of cultures with different world views. Three-dimensional

animations of movements through space can enhance understanding of architecture on human

behavior or of archaeological structures and their human uses in ancient times. Virtual models,

visualizations, and moving objects provide a rich approach to sensing changing forms and roles

of landscapes through time. However, there are issues that require extended research if we are to

validate the value of investments in multidimensional and dynamic visualizations and models.

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Multidimensional visualizations provide spatial “eye candy,” but we know far too little

about the processes by which humans extract meaning and learn from such visualizations and

from spatial data more generally, and about ways to improve those processes. Attention is

needed to discover research-based principles for how to design multimedia material (i.e., the

science of instruction) and to formulate a research-based theory of how people learn from words

and pictures (i.e., the science of learning concerned with the nature of spatial thinking in

complex cognitive activities such as comprehension, reasoning, and problem solving). Research

is needed to determine how people learn about spaces through direct experiences, maps, and

other visualizations, and about how individual and group differences impact spatial thinking and

spatial abilities more generally. The work of the Spatial Intelligence and Learning Center

(SILC), a multi-campus and multi-disciplinary research team from Temple University,

Northwestern University, the University of Pennsylvania, the University of Chicago, and

Chicago Public Schools, is especially important in documenting the learning outcomes from

different ways of visualizing spatial information (see http://spatiallearning.org/). SILC is funded

by the U.S. National Science Foundation as one of six Science of Learning Centers.

5. Moving beyond spatially intuitive thinking

Students are familiar with virtual spaces and the power of imaging through video games and

digital movies. Whereas such experience may add to one's to spatial skill set, it does not obviate

a need for formal exposure and in-depth understanding. Yet, although one would insist that a

student learn something of statistical theory before using statistical software, the same is not true

of spatial software -- in the spatial arena, the development of relevant theory and concepts has

lagged far behind, and it is clear that a wide gap exists between the power and accessibility of

tools on the one hand and the ability of researchers, students, and the general public to make

effective use of them on the other. Examples abound.

To give one simple case, the NRC (2006) report on Learning to Think Spatially

documents a 2003 article in The Economist (5/3/2003) in which mapping software was used to

create a colorful illustration for a news story about the North Korean missile threat. Different

missile ranges were depicted as concentric circles on a Mercator projection, despite that

projection’s severe distortions at high latitudes (on the Mercator projection the Poles are at

infinity). When the map was corrected in a subsequent issue (5/17/2003), the 10,000-km missiles

that according to the first map could barely reach the western Aleutians were shown to reach of

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the North Pole and to have Minneapolis comfortably in range. The distortions introduced by

flattening curved spaces are but one of a host of spatial concepts that affect the use of these

powerful technologies, and must be part of the training of researchers and educators across the

full range of disciplines, something that is rarely seen in traditional approaches to curricula.

The growing body of literature on spatial concepts, in disciplines as diverse as cognitive

psychology, mathematics, geography, and philosophy, identifies and enumerates basic elements

of a spatial perspective. Some of these concepts, such as distance and containment, are acquired

informally in early childhood, whereas others are encountered or formalized much later, or

remain problematic even to graduate students. Several researchers have published lists of such

concepts. Gersmehl (2005), for example, lists 13 spatial concepts as fundamental to a

geographical perspective, while Newcombe and Huttenlocher (2000) list 11 spatial concepts as

fundamental to their work at SILC on the development of spatial cognition. Mitchell (1999,

2005) and de Smith, Goodchild, and Longley (2006) organized their introductions to the analysis

of spatial data using GIS around spatial concepts. On the other hand, the actual design of GIS

user interfaces continues to be driven more by legacy and implementation than by any

fundamental conceptual organization -- which may explain why much GIS software has a

reputation for being difficult to use.

At the Center for Spatial Studies, University of California, Santa Barbara, we have

developed and published online a basic ontology of spatial concepts

(http://www.teachspatial.org), linking each entry to its original sources. We have scanned the

literature of many disciplines from geography and psychology to architecture in this effort, and

have to date documented 186 such concepts. We have developed several organizing schemata to

give structure to the collection, including hierarchical relationships (some concepts are subsets of

others), semantic similarity (some concepts have different names in different disciplines), and

formality (some concepts are formalizations of other intuitive concepts). As we continue to

develop the site, we plan to include instructional materials focused on advanced concepts for

critical spatial thinking. We use the word critical in the sense of reflective, skeptical, or analytic,

implying that the successful application of spatial perspectives can never be rote, but must

always involve the mind of the researcher in an active questioning and examination of

assumptions, techniques, and data if it is to meet the rigorous standards of good scholarship.

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One way to define critical spatial thinking is in relation to the use of spatial tools and data

-- as the mental processes that accompany the use of these technologies. Critical spatial thinking

is in sharp contrast to rote button-pushing, and implies that the processes of data manipulation,

analysis, data mining, and modeling provoke and require critical thinking, about such

comparatively profound issues as scale, accuracy, uncertainty, ontology, representation,

complexity, projection, and ethics. We see spatial technologies as an essential, integrating

element that cuts across disciplines through common language and concepts.

The remainder of this section discusses a selection of these spatial concepts, focusing on

three that are of an advanced nature, and are typically acquired during senior undergraduate or

graduate education, if ever. We discuss them here as examples of the concepts that are needed to

underpin the critical spatial thinking skills that we might expect of spatially aware scholars.

Anselin (1989) identified two properties as particularly important in the analysis of

spatial data, but likely to cause conceptual difficulty even at the graduate level. Spatial

heterogeneity refers to the tendency for phenomena distributed in many spaces, notably the space

of the Earth’s surface, to be statistically non-stationary. Spatial heterogeneity confounds attempts

to generalize from spatial samples, because results of an analysis of a limited area will change

when the boundaries of the area are shifted. Instead specially adapted methods have been

developed in recent years that are place-based and local, yielding results such as model

parameter values that vary spatially (Anselin, 1995; Fotheringham, Brunsdon, and Charlton,

2002). These techniques represent a radical rethinking of the traditional nomothetic demand of

science that gives greatest significance to results that are true everywhere, at all times. Anselin’s

second concept, spatial dependence, refers to the tendency for spatial data to exhibit short-run

spatial autocorrelation, a property that forms the basis of the fields of geostatistics and spatial

statistics (Cressie, 1993; Haining, 2003). Unless it is addressed explicitly, spatial dependence can

lead to artificially inflated degrees of freedom and the enhanced possibility of Type I statistical

errors (rejection of the null hypothesis when it is true). These concepts are also well recognized

in the analysis of time series.

Although the origins of statistical theory lie in the controlled experiments of pioneers

such as R.A. Fisher, disciplines across the social and environmental sciences frequently deal with

data gathered from experiments that are natural, relying on data over which the investigator has

little or no control. Because these sciences frequently deal with data that are framed in space and

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time, they encounter the issues associated with spatial dependence and spatial heterogeneity. Yet

students in these disciplines learn essentially the same introductory perspective on statistical

theory as those in experimental psychology, and little attention is given to the special properties

of spatial data. The concepts addressed by Anselin’s two properties ensure that an analysis of

social data from the census tracts of a city, or ecological data from field plots, will be unlikely to

justify the traditional assumptions of random and independent sampling from some real or

imagined population.

One of the most problematic spatial concepts is scale, in both of its dual meanings of

extent and resolution. Dependence of results on extent, as well as the difficulties of

generalization from any limited area, have already been addressed in the context of spatial

heterogeneity. Resolution addresses the impossibility of a perfect representation of the infinite

complexity of many spatially distributed phenomena, and the consequent necessity for

generalization, approximation, sampling, or other mechanisms to remove detail. Scale issues

tend to be compounded by the spatial resolution of acquisition systems, which may have little to

do with the spatial resolution needed for accurate analysis and modeling, or for effective decision

making (for discussions of the common issues of scale across diverse contexts see, for example,

Levin, 1992; Quattrochi and Goodchild, 1997; Tate and Atkinson, 2001; Mandelbrot, 1982). In

spatial analysis in the social sciences, scale and related issues are recognized in the form of the

ecological fallacy (King, 1997; Robinson, 1950) and the modifiable areal unit problem

(Openshaw, 1983). We consider it essential that scale-related issues be part of the critical

frameworks of researchers in any discipline working with spatially aggregated data.

6. The education challenge

We find that students are inadequately trained in the challenges of working with

phenomena embedded in space and time, and that there is a need to engage them both in research

on advancing the theory and technique of critical spatial thinking, and in applying critical

thinking to research in a range of disciplines if they are to develop as leaders of a spatially

enabled scholarship that is better prepared to use the evolving technologies, and better equipped

to exploit the growing flood of spatially referenced data.

The problems of statistical inference from spatial data provide an example of the need for

critical spatial thinking. Issues abound in the use of directional statistics, and in directional

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anisotropy in spatial covariances. Critical spatial thinkers understand the assumptions underlying

spatial data and the effects of scale and non-stationarity on research outcomes. They appreciate

the difficulties of inference from multidimensional data when they are subject to dimensionality

reduction and the problems and implications of uncertainty in spatial data that might leave their

users uncertain about the true nature of the world they represent. In addition, critical spatial

thinkers can use geostatistical theory to provide a more rigorous basis for interpolation in

spatiotemporal data.

Reference has already been made to a general lack of preparation in critical spatial

thinking in our education system. Although spatial tasks such as block manipulation are common

features of intelligence tests, it is rare to find students being prepared for them in any systematic

way. One can speculate about the reasons for this. Perhaps spatial thinking is regarded as innate,

an unmalleable skill possessed by some and not others (there are documented links between

some spatial skills and gender, for example; Voyer, Voyer, and Bryden 1995); or perhaps spatial

skills are regarded as trivial, acquired in early childhood, and in no way comparable to

mathematical, logical, or verbal skills.

A recent report of the National Research Council (NRC, 2006) defined spatial thinking as

“a cognitive skill that can be used in everyday life, the workplace, and science to structure

problems, find answers, and express solutions using the properties of space. It can be learned and

taught formally to students using appropriately designed tools, technologies, and curricula.” The

report documented the lack of attention to spatial thinking in formal curricula, despite assertions

that it is a primary form of intelligence (Eliot 1987; Gardner 1983), and called for “a national

initiative to integrate spatial thinking into existing standards-based instruction across the school

curriculum, such as in mathematics, history, and science classes...to create a generation of

students who learn to think spatially in an informed way.” The report viewed spatial thinking as

“an amalgam of three elements: concepts of space, tools of representation, and processes of

reasoning” (p. 12). Although focused on K-12 education, the report includes a series of rich and

compelling examples of the application of spatial perspectives whose applicability extends

across disciplines and all levels of education. A recent article in Science (Holden 2009) quotes

David Lubinski of Vanderbilt University from a presentation to a National Science Board

workshop on innovation: “(D)espite their importance in science, particularly in fields such as

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engineering, robotics, or astronomy, spatial abilities are getting short shrift both in school

curricula and in programs trying to spot precocious youths.”

Dating back to the work of Smith (1964), findings continue to show that individuals who

are more spatially adept have greater success in higher-level problem solving (Kozhevnikov et

al., 1999, 2002, 2005). SILC has assembled evidence that spatial intelligence can be enhanced,

and that heightened spatial abilities among adolescents can be a predictor of future science career

paths (Shea, Lubinski, and Benbow, 2001). Legé (1999) has argued that lack of spatial awareness

and skills hinders students’ ability to perform many tasks that are essential in the science and

engineering disciplines, while Wheatley (1997) argues that advancing teachers’ knowledge of the

efficacy of such spatial skills as visualization can help students to become better solvers of math

problems.

We live in a global academic world that is dominated by the need to solve complex

problems that are embedded in space and time, and to bring spatial perspectives to scholarship.

This 21st century world is collaborative, enabled by cyberinfrastructure, and is highly

interdisciplinary. It is evident that students should be trained to the standards of a critical spatial

thinker, including:

• the potential to contribute critical spatial understanding to research at the interface

between disciplines;

• the ability to work in a team;

• the ability to explain the space-time context of research to non-experts;

• the ability to develop new and highly original spatially informed research ideas;

• the experience to enable sustained and successful research dialog within an

international community of spatially aware scientists;

• the ability to disseminate spatial understanding of research through teaching and

curriculum development at K-12 and undergraduate levels; and

• the ability to transfer spatial technologies and spatial concepts for research across

different knowledge domains and problem sets.

Achieving these goals will require a combination of conventional course-based

curriculum, intensive peer-to-peer interaction, project-based learning, and engagement with

17

activities across the educational spectrum (in the community and region, on campuses,

nationally, and internationally).

References

Anonymous (2008). Editorial: A place for everything. Nature 453(2).

Anselin, L. (1989). What Is Special about Spatial Data? Alternative Perspectives on Spatial

Data Analysis. Technical Report 89-4. Santa Barbara, CA: National Center for Geographic

Information and Analysis.

Anselin, L. (1995). Local indicators of spatial association -- LISA. Geographical Analysis 27:

93-115.

Anselin, L., Florax, R. J., & Rey, S. J., Eds. (2004). Advances in Spatial Econometrics:

Methodology, Tools and Applications. Berlin: Springer.

Ayers, W. (2009). Space, and time: mapping historical change. Keynote presentation at the GIS

in the Humanities and Social Sciences International Conference, Research Center for

Humanities and Social Sciences, Academia Sinica, Taipei, Taiwan. October 7-9, 2009.

Boonstra, O. W. A. (2009). No place in history—Geo-ICT and historical science. In H. J.

Scholten, R. van de Velde, & N. van Manen (Eds.) Geospatial Technology and the Role of

Location in Science, pp. 87-101. Dordrecht, NL: Springer.

Castro, M. C. (2007). Spatial demography: An opportunity to improve policy making at diverse

decision levels. In P. R. Voss (Ed.) Population Research and Policy Review 26 (Special issue

on spatial demography): 477-509.

Cressie, N. A. C. (1993). Statistics for Spatial Data. New York: Wiley.

Cromley, E. K., & McLafferty, S. L. (2002). GIS and Public Health. New York: Guilford Press.

Cummins, S., Curtis, S., Diez-Roux, A.V., & Macintyre, S. (2007). Understanding and

representing 'place' in health research: A relational approach. Social Science and Medicine

65: 1825-38.

de Smith, M. J., Goodchild, M. F., & Longley, P. A. (2009). Geospatial Analysis. A

Comprehensive Guide to Principles, Techniques, and Software Tools. Leicester, U.K: The

Winchelsea Press, Troubador Publishing, Ltd. (a pdf e-book at

http://www.spatialanalysisonline.com/).

18

Eliot, J. (1987). Models of Psychological Space: Psychometric, Developmental and Experimental

Approaches. New York: Springer-Verlag.

Elwood, S. (2008).Volunteered geographic information: Key questions, concepts and methods to

guide emerging research and practice. GeoJournal 72: 133-135.

Fotheringham, A. S. (2009). Spatial variations in population dynamics: A GIScience and GWR

perspective using a case study of Ireland 1841-1851. Special presentation at the GIS in the

Humanities and Social Sciences International Conference, Research Center for Humanities

and Social Sciences, Academia Sinica, Taipei, Taiwan. October 7-9, 2009.

Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted

Regression. The Analysis of Spatially Varying Relationships. New York: John Wiley.

Gardner, H. E. (1983). Frames of Mind: The Theory of Multiple Intelligences. New York: Basic

Books.

Gersmehl, P. J. (2005). Teaching Geography. New York: Guilford.

Goodchild, M. F. (2007). Citizens as sensors: the world of volunteered geography. GeoJournal

69 (4): 211-221.

Goodchild, M. F. (2009). The changing face of GIS. Keynote presentation at the GIS in the

Humanities and Social Sciences International Conference, Research Center for Humanities

and Social Sciences, Academia Sinica, Taipei, Taiwan. October 7-9, 2009.

Goodchild, M. F., Anselin L., & Deichmann, U. (1993). A framework for the areal interpolation

of socioeconomic data. Environment and Planning A 25: 383-397.

Goodchild, M. F., Egenhofer, M. J., Fegeas, R., and Kottman, C. A., Eds. (1999). Interoperating

Geographic Information Systems. Boston: Kluwer Academic Publishers.

Goodchild, M. F., and Janelle, D. G., Eds. (2004). Spatially Integrated Social Science. New

York: Oxford University Press.

Gregory, I. (2009). Censuses, literature and newspapers: quantitative and qualitative approaches

to studying the past with GIS. Keynote presentation at the GIS in the Humanities and Social

Sciences International Conference, Research Center for Humanities and Social Sciences,

Academia Sinica, Taipei, Taiwan. October 7-9, 2009.

Haining, R. P. (2003). Spatial Data Analysis: Theory and Practice. New York: Cambridge

University Press.

19

Harris, T. (2009). Conceptualizing the spatial humanities and humanities GIS. Keynote

presentation at the GIS in the Humanities and Social Sciences International Conference,

Research Center for Humanities and Social Sciences, Academia Sinica, Taipei, Taiwan.

October 7-9, 2009.

Holden, C. (2009). Science needs kids with vision. Science 325: 1190-1191.

Hornsby, K. S. and Yuan, M., Eds. (2008). Understanding Dynamics of Geographic Domains.

Boca Raton: CRC Press.

Janelle, D. G. (2009). Spatio-temporal approaches to understanding human behavior and social

organization. Special presentation at the GIS in the Humanities and Social Sciences

International Conference, Research Center for Humanities and Social Sciences, Academia

Sinica, Taipei, Taiwan. October 7-9, 2009.

Janelle, D. G., & Hodge, D. C., Eds. (2000). Information, Place, and Cyberspace: Issues in

Accessibility. Berlin: Springer-Verlag.

Jessop, M. (2007). Literary and linguistic computing advance access. Literary and Linguistic

Computing. Published online on November 20, 2007, doi:10.1093/llc/fqm041,

http://llc.oxfordjournals.org/cgi/content/short/fqm041v1.

Johnson, S. (2006). The Ghost Map: The Story of London’s Most Terrifying Epidemic and How

It Changed Science, Cities, and the Modern World. New York: Riverhead Books.

King, G. (1997). A Solution to the Ecological Inference Problem: Reconstructing Individual

Behavior from Aggregate Data. Princeton, NJ: Princeton University Press.

Kozhevnikov, M., Hegarty, M., & Mayer, R. (1999). Students’ use of imagery in solving

qualitative problems in kinematics. Washington DC: US Department of Education (ERIC

Document Reproduction Service No. ED433239).

Kozhevnikov, M., Hegarty, M., & Mayer, R. (2002). Revising the visualize-verbalizer

dimension: Evidence for two types of visualizers. Cognition and Instruction 20(1): 47-77.

Kozhevnikov, M., Kosslyn, S., & Shephard, J. (2005). Spatial versus object visualizers: A new

characterization of visual cognitive style. Memory and Cognition 33(4): 710-726.

Krugman, P. (1991). Geography and Trade. Cambridge: MIT Press.

Legé, S. (1999). Why not three dimensions? Mathematics Teacher 92 (7): 560-563.

Levin, S.A. (1992). The problem of pattern and scale in ecology. Ecology 73 (6): 1943-1967.

20

Lin, H. and Batty, M., Eds. (2009. Virtual Geographic Environments. Beijing: Science Press.

Liu, Y., Guo, Q. H., Wieczorek, J., & Goodchild, M. F. (in press). Positioning localities based on

spatial assertions. International Journal of Geographical Information Science.

Mandelbrot, B. (1982). The Fractal Geometry of Nature. San Francisco: Freeman.

Matthews, S. A. (2008). The salience of neighborhoods: Lessons from early sociology?

American Journal of Preventive Medicine 34 (3): 257-259.

Mitchell, A. (1999). The ESRI Guide to GIS Analysis: Vol. 1, Geographic Patterns and

Relationships. Redlands, CA: ESRI Press.

Mitchell, A. (2005). The ESRI Guide to GIS Analysis: Vol. 2, Spatial Measurements and

Statistics. Redlands, CA: ESRI Press.

National Research Council. (2006). Learning to Think Spatially: GIS as a Support System in the

K-12 Curriculum. Washington, DC: National Academies Press.

http://www.nap.edu/catalog.php?record_id=11019.

Newcombe, N. S. and Huttenlocher, J. (2000). Making Space. Cambridge, MA: MIT Press.

Nyerges, T., Couclelis, H., & McMaster, R., Eds. (in press). Handbook on GIS and Society

Research. Los Angeles: Sage Publications.

Openshaw, S. (1983). The modifiable areal unit problem. Concepts and Techniques in Modern

Geography: CATMOG Series 38. Norwich, UK: GeoBooks.

Orszag, P. R., Barnes, M., Carrion, A., & Summers, L. (2009). Memorandum for the Heads of

Executive Departments and Agencies. The White House, Washington, D.C., August 11,

2009. http://www.whitehouse.gov/omb/assets/memoranda_fy2009/m09-28.pdf

Phoenix, M. (2009). The Importance of spatial thinking in social sciences. Special presentation at

the GIS in the Humanities and Social Sciences International Conference, Research Center

for Humanities and Social Sciences, Academia Sinica, Taipei, Taiwan. October 7-9, 2009.

Quattrochi, D. A. and Goodchild, M. F., Eds. (1997). Scale in Remote Sensing and GIS. Boca

Raton, FL: Lewis.

Robinson, W.S. (1950). Ecological correlations and the behavior of individuals. American

Sociological Review 15: 351-357.

Rumsey, A. S. (2009). Scholarly Communication Institute 7: Spatial Technologies and the

Humanities, a conference hosted by the Scholarly Communication Institute, University of

21

Virginia, Charlottesville, VA: June 28-30, 2009. Accessed at http://www.uvasci.org/wp-

content/uploads/2009/10/sci7-published-full1.pdf.

Scholten, H. J., van de Velde, R., & van Manen, N., Eds. (2009). Geospatial Technology and the

Role of Location in Science. Dordrecht, NL: Springer.

Shea, D. L., Lubinski, D., & Benbow, C. P. (2001). Importance of assessing spatial ability in

intellectually talented young adolescents: A 20-year longitudinal study. Journal of

Educational Psychology 93 (3): 604-614.

Skupin, A. & Fabrikant, S. (2003). Spatialization methods: A cartographic research agenda for

non-geographic information visualization. Cartography and Geographic Information Science

30 (2): 99-119.

Smith, I. A. (1964). Spatial Ability: Its Educational and Social Significance. London: University

Press.

Tate, N. J. and Atkinson, P. M., Eds. (2001). Modelling Scale in Geographical Information

Science. New York: Wiley.

Tilman, D. & Kareiva, P., Eds. (1997). Spatial Ecology: The Role of Space in Population

Dynamics and Interspecific Interactions. Princeton, NJ: Princeton University Press.

Tobler, W. R., Deichmann, U., Gottsegen, J., & Maloy, K. (1997). World population in a grid of

spherical quadrilaterals. International Journal of Population Geography 3: 203-225.

Voss, P. R. (2007). Demography as a spatial social science. In P. R. Voss (Ed.) Population

Research and Policy Review 26 (Special issue on spatial demography): 457-476.

Voss, P. R., White, K. J. C., & Hammer, R. B. (2006). Explorations in spatial demography. In W.

Kandel & D. L. Brown (Eds.) The Population of Rural America. Demographic Research for

a New Century. Dordrecht, The Netherlands: Springer.

Voyer, D., Voyer, S., and Bryden, M. P. (1995). Magnitude of sex differences in spatial abilities:

a meta-analysis and consideration of critical variables. Psychological Bulletin 117 (2): 250-

270.

Wheatley, G. H. (1997). Reasoning with images in mathematical activity. In L. D. English (Ed.),

Mathematical Reasoning: Analogies, Metaphors, and Images, pp. 281-297. Mahwah, NJ:

Erlbaum.